U.S. patent application number 14/773878 was filed with the patent office on 2016-01-28 for system and signatures for the multi-modal physiological stimulation and assessment of brain health.
The applicant listed for this patent is Adam J. SIMON. Invention is credited to Adam J. SIMON.
Application Number | 20160022167 14/773878 |
Document ID | / |
Family ID | 51538282 |
Filed Date | 2016-01-28 |
United States Patent
Application |
20160022167 |
Kind Code |
A1 |
SIMON; Adam J. |
January 28, 2016 |
SYSTEM AND SIGNATURES FOR THE MULTI-MODAL PHYSIOLOGICAL STIMULATION
AND ASSESSMENT OF BRAIN HEALTH
Abstract
A system and diagnostic signatures which are derived from the
data collected in the system captures multiple streams of
biological sensor data for assessing brain health and functionality
of a user. The system includes a plurality of biological sensors
adapted to collect biological sensor data from the user as well as
the ability to stimulate the brain in a variety of sensory,
cognitive, physical, and chemical challenges. Several of the
biological sensors are accommodated in an electronics module
mounted on the user's head.
Inventors: |
SIMON; Adam J.; (Yardley,
PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SIMON; Adam J. |
Yardley |
PA |
US |
|
|
Family ID: |
51538282 |
Appl. No.: |
14/773878 |
Filed: |
March 14, 2014 |
PCT Filed: |
March 14, 2014 |
PCT NO: |
PCT/US14/28061 |
371 Date: |
September 9, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61799842 |
Mar 15, 2013 |
|
|
|
61836294 |
Jun 18, 2013 |
|
|
|
61932915 |
Jan 29, 2014 |
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Current U.S.
Class: |
600/301 |
Current CPC
Class: |
A61B 5/4088 20130101;
A61B 5/024 20130101; A61B 5/0533 20130101; A61B 5/1118 20130101;
A61B 5/16 20130101; A61B 5/04845 20130101; A61B 5/163 20170801;
A61B 5/4064 20130101; A61B 5/04847 20130101; A61B 5/6803 20130101;
A61B 5/02405 20130101; A61B 5/04842 20130101; A61B 5/14542
20130101; A61B 5/0478 20130101 |
International
Class: |
A61B 5/0484 20060101
A61B005/0484; A61B 5/024 20060101 A61B005/024; A61B 5/053 20060101
A61B005/053; A61B 5/16 20060101 A61B005/16; A61B 5/00 20060101
A61B005/00; A61B 5/0478 20060101 A61B005/0478; A61B 5/145 20060101
A61B005/145 |
Claims
1. A system for capturing multiple streams of biological sensor
data for assessing brain health of a user, comprising: an
electronics module mounted on or near the user's head including an
active brainwave sensor that collects at least one channel of EEG
brainwave data a plurality of biological sensors that
simultaneously record biological sensor data from the user using a
plurality of biological sensors, said biological sensors including
a microphone that records human speech to capture verbal responses
of the human subject during a battery of tasks to either cognitive
challenges or auditory stimulations and an image sensor that
records that records eye movements, eye saccade and other biometric
identification information; and a stimulation device that applies
at least one of a visual stimulant, an auditory stimulant, a
gastronomic stimulant, an olfactory stimulant, and/or a motion
stimulant to the user, wherein the plurality of biological sensors
simultaneously measure the body's response to stimulants applied by
said stimulation device for recordation by said electronic
module.
2. The system of claim 1, wherein said plurality of biological
sensors generate one or more additional biological sensor data
streams selected from among accelerometer measures of balance and
movement, pulse oximetry measurements of heart rate, heart rate
variability, and arterial oxygen, Galvanic Skin conductance (or
Dermal Skin conductance) for emotional and mood information,
cognitive data in the form of key strokes, and mouse clicks or
touch screen events during cognitive challenges.
3. The system of claim 2, further comprising a peripheral MCU in
the form of a laptop computer, tablet PC or smartphone like device
that simultaneously captures biological signal streams collected by
said plurality of biological sensors.
4. The system of claim 1, further comprising at least one
peripheral electronics module that is positioned on the trunk or
limbs of the user to collect position and heart rate data that is
co-registered in time with data collected by said electronics
module so that the collected biological sensor data can be analyzed
either alone in isolation or in a cross-correlative fashion.
5. The system of claim 1, wherein the electronics module further
comprises LEDs for photic stimulation.
6. The system of claim 1, further comprising a peripheral device
that displays images or movies to the user to stimulate the visual
system while the biological sensors collect the user's brain
response to the stimulation.
7. The system of claim 1, wherein the electronics module includes
multi-contact electrodes whereby a standard circle or square
electrode is equally divided into 2, 3 or 4 equivalent but
independent electrodes.
8. The system of claim 1, wherein the electronics module includes a
mass storage device for storage of collected biological sensor
data.
9. The system of claim 1, wherein the stimulation device applies
stimuli to at least one of the user's senses and the electronics
module collected biological sensor data from biological sensors
that collect biological sensor data from another of the user's
senses.
10. The system of claim 9, wherein the stimulation device presents
photographic images to the user while the electronics module
collects skin conductance measurements, brainwave EEG, and/or
accelerometer measurements while the photographic images are
presented.
11. The system of claim 1, wherein the battery of tasks consists
essentially of "Do you have a Headache", "Don't Feel right",
"Feeling Slowed down", "In a Fog", "Pressure in the head", "Dizzy",
"Difficulty Concentrating", "Fatigue", and "Drowsy" questions of a
Graded Symptom Checklist.
12. The system of claim 1, wherein the battery of tasks includes
Immediate and Delayed Memory tasks of a Standard Assessment of
Concussion.
13. The system of claim 12, wherein the battery of tasks further
includes a Concentration Task of the Standard Assessment of
Concussion.
14. The system of claim 11, wherein the battery of tasks includes
only three foam based postures of the BESS total error score.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application claims benefit of U.S. Provisional
Application No. 61/799,842 filed Mar. 15, 2013, U.S. Provisional
Application No. 61/836,294 filed Jun. 18, 2013, and U.S.
Provisional Application No. 61/932,915 filed Jan. 29, 2014. The
contents of those patent applications are hereby incorporated by
reference in their entireties.
TECHNICAL FIELD
[0002] The invention relates to diagnosis and analysis of brain
health through the use of activated tasks and stimuli in a system
to dynamically assess one's brain state and function.
BACKGROUND
[0003] Normal functioning of the brain and central nervous system
is critical to a healthy, enjoyable and productive life. Disorders
of the brain and central nervous system are among the most dreaded
of diseases. Many neurological disorders such as stroke,
Alzheimer's disease, and Parkinson's disease are insidious and
progressive, becoming more common with increasing age. Others such
as schizophrenia, depression, multiple sclerosis and epilepsy arise
at younger age and can persist and progress throughout an
individual's lifetime. Sudden catastrophic damage to the nervous
system, such as brain trauma, infections and intoxications can also
affect any individual of any age at any time.
[0004] Most nervous system dysfunction arises from complex
interactions between an individual's genotype, environment and
personal habits and thus often presents in highly personalized
ways. However, despite the emerging importance of preventative
health care, convenient means for objectively assessing the health
of one's own nervous system have not been widely available.
Therefore, new ways to monitor the health status of the brain and
nervous system are needed for normal health surveillance, early
diagnosis of dysfunction, tracking of disease progression and the
discovery and optimization of treatments and new therapies.
[0005] Unlike cardiovascular and metabolic disorders, where
personalized health monitoring biomarkers such as blood pressure,
cholesterol, and blood glucose have long become household terms, no
such convenient biomarkers of brain and nervous system health
exist. Quantitative neurophysiological assessment approaches such
as positron emission tomography (PET), functional magnetic
resonance imaging (fMRI) and neuropsychiatric or cognition testing
involve significant operator expertise, inpatient or clinic-based
testing and significant time and expense. One potential technique
that may be adapted to serve a broader role as a facile biomarker
of nervous system function is a multi-modal assessment of the brain
from a number of different forms of data, including
electroencephalography (EEG), which measures the brain's ability to
generate and transmit electrical signals. However, formal lab-based
EEG approaches typically require significant operator training,
cumbersome equipment, and are used primarily to test for
epilepsy.
[0006] Alternate and innovative biomarker approaches are needed to
provide quantitative measurements of personal brain health that
could greatly improve the prevention, diagnosis and treatment of
neurological and psychiatric disorders. Unique multi-modal devices
and tests that lead to biomarkers of Parkinson's disease,
Alzheimer's disease, concussion and other neurological and
neuropsychiatric conditions is a pressing need.
SUMMARY
[0007] A system and diagnostic signatures which are derived from
the data collected in the system address the above needs in the art
by capturing multiple streams of biological sensor data for
assessing brain health and functionality of a user. In an exemplary
embodiment, the system includes a plurality of biological sensors
adapted to collect biological sensor data from the user as well as
the ability to stimulate the brain in a variety of sensory,
cognitive, physical, and chemical challenges. The biological
sensors include an active brainwave sensor that collects at least
one channel of EEG brainwave data in addition to one or more
additional biological sensor data streams selected from among
accelerometer measures of balance and movement, microphone
measurements of voice and response, image sensor to track eye
movement and biometric identification, pulse oximetry measurements
of heart rate, heart rate variability, and arterial oxygen,
Galvanic Skin Response (or Dermal Skin Conductance) for emotional
and mood information, cognitive data in the form of key strokes,
and mouse clicks or touch screen events during cognitive
challenges. Lastly, regulatory agency approved drugs, ingredients,
and compounds can be administered in a diagnostic capacity to
challenge the brain and diagnostically measure the response.
[0008] In one embodiment, the system includes only one reusable
electronic module (REM) module proximal to the brain for recording
various biological signal streams of data. This is complemented by
various biological signal streams collected simultaneously in a
peripheral MCU in the form of a laptop computer, tablet PC or
smartphone like device.
[0009] In another embodiment, the system includes one or more REM
module(s) in addition to the REM module on the head. In this
embodiment, a non-head REM module is positioned on the trunk of the
human subject to collect position and heart rate information or,
alternatively, or in addition, placed on the wrist or ankle of a
limb in order to record biological signals from the extremities of
the individual. In all cases, the data are co-registered in time so
that each modality or biological signal can be analyzed either
alone in isolation or in a cross correlative fashion. Multi-variate
predictive statistical models can be built with the diagnostic
information to help the health and wellness of the human subject
under assessment.
[0010] The system also has the means to stimulate the human subject
under assessment for their response to sensory, cognitive,
physical, and chemical challenges. In one embodiment of the
invention, the visual system is assessed with either (i) photic
stimulation from either a peripheral MCU or head REM or (ii) images
or movies displayed on the video screen of a peripheral MCU. In
another embodiment, the auditory system is challenged with binaural
beats, mono aural beats, isochronic tones or other important
auditory stimulation with a known or expected biomarker signature
within the multi-modal streams of data. In another embodiment, the
gastronomic system is stimulated with either a dose of specialized
food product for consumption or, alternatively, directly with a
tongue electrical stimulation device. In yet another embodiment,
the olfactory system is stimulated via scratch and sniff cards,
automated aroma delivery systems or direct electrical stimulation
of the olfactory bulb. Lastly, the sense of touch can be stimulated
via known textures or through direct transcutaneous electrical
stimulation. It is part of the present invention that any of these
embodiments can be practiced alone or in combination as may be
desired and advantageous.
[0011] An alternate embodiment of the invention includes various
multi-contact electrodes whereby a standard circle or square is
equally divided into 2, 3 or 4 equivalent but independent
electrodes. In doing so, a 2 electrode system of the present
invention may become a 4, 6 or 8 electrode system within the same
spatial and temporal configuration, including the form factor of a
headband.
[0012] One embodiment includes the use of a disposable air pillow
or cushion or other compact yet expandable device to create an
irregular or unstable surface for human subjects to try to balance
on to assess static balance/stability or move across to assess
dynamic balance/stability.
[0013] In another embodiment, additional data transducers are built
into the REM module such that the system can acquire diverse
streams of biological sensor data. One particular embodiment
includes the inclusion of either an acoustic microphone and/or a
forward facing digital image sensor (essentially a movie
camera).
[0014] Another embodiment includes use of either an image sensor
for image processing derived eye tracking and movement or a more
dedicated device or technology, much like the Google Glass
eye-tracker or an infra-red based eye tracker.
[0015] In another embodiment, the REM is designed with a mass
storage device such as a microSD card or other high density RAM
storage unit. This RAM storage unit enables data collection from
the REM directly to mass storage without the need for a wireless
connection to a peripheral MCU.
[0016] In yet another embodiment of the invention, photographic
images with unique emotional and valence based characteristics are
shown to human subjects while their biological signals are measured
and recorded. In this case, those without normal emotional response
to fanciful images (a pig flying over an ocean) can be objectively
detected by the biological sensor data streams. Other mood and
emotional information can be advantageously collected. In one
particular embodiment, galvanic skin response (GSR) measurements
are gathered at the same time that brainwave EEG and accelerometer
measurements are collected while photographic images are presented.
This could equally work for dynamic images such as movies rather
than static images.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Embodiments of the invention can be better understood with
reference to the following drawings.
[0018] FIG. 1 is a schematic diagram illustrating a human body
outfitted with multiple REM modules as well as a nearby peripheral
microprocessor (MCU) with direct or wireless access to electronic
medical records.
[0019] FIG. 2 is a schematic diagram illustrating the flow of data
from the human subject wearing headset to the laptop, tablet or
smartphone where it is encrypted and transmitted to the cloud.
[0020] FIG. 3 is a schematic diagram illustrating the arrival of
the encrypted data package where it is decrypted, passed through
signal pre-processing for artifact detection, then through various
signal processing modules for biometric feature table assembly and
predictive analytics.
[0021] FIG. 4 is a schematic illustration of the diagnostics as a
service system.
[0022] FIG. 5 is a schematic illustration of a series of nine
different bio signals from a multi-modal stimulation and data
acquisition system.
[0023] FIG. 6 is a schematic illustration of a series of nine
different biological signals from a multi-modal stimulation and
data acquisition system. (Note: synthetic data, not from real human
subjects).
[0024] FIG. 7 is a schematic illustration of a one channel
regulatory compliant device.
[0025] FIG. 8 is a schematic illustration of a headband with
alternate electrode placements at each temple.
[0026] FIG. 9A is a schematic illustration of single circular or
square electrode that has been divided into two equivalent but
adjacent electrodes in the same amount of space.
[0027] FIG. 9B is a schematic illustration of single circular or
square electrode that has been divided into three equivalent
adjacent electrodes in the same amount of space.
[0028] FIG. 9C is a schematic illustration of single circular or
square electrode that has been divided into four equivalent
adjacent electrodes in the same amount of space.
[0029] FIG. 10 is a schematic illustration of a headband supported
electronics module with both a microphone and small camera embedded
into the module.
[0030] FIG. 11 is a schematic illustration of a Google Glass like
device with infra-red eye tracking capability.
[0031] FIG. 12 schematic illustration of a headband supported
electronics module with both a dual LED, 3-color montage LED and
any array of LED point sources for photic stimulation.
[0032] FIG. 13 is a top down schematic view of an electrical tongue
stimulator for the brain.
[0033] FIG. 14 is a top down schematic magnified view of an
electrical tongue stimulator for the brain.
[0034] FIG. 15 is a top down schematic magnified view of an
electrical tongue stimulator for the brain with the availability of
a sterile package via a disposable sheath which enables re-use of
the main electrical components.
[0035] FIG. 16 is a top down schematic view of an electrical nose
stimulator for the brain.
[0036] FIG. 17 is a top down schematic view of an electrical nose
stimulator for the brain with disposable sheaths to enable re-use
of the main electrical components.
[0037] FIG. 18 is a pair of graphical displays of a logistic plot
and its corresponding Receiver Operating Characteristic curve (ROC)
of an EEG feature (relative beta) used to predict the clinical
diagnosis of concussion subjects versus control subjects.
[0038] FIG. 19 is a pair of graphical displays of the Receiver
Operating Characteristic curve (ROC) of an EEG feature (relative
beta) combined with a cognitive task score from the King-Devick
test as a pair (upper plot) or in combination with two co-variates,
age and gender (lower plot), a multi-modal predictive model
consistent with the present invention. The Area Under the Curve
(AUC) is shown as well.
[0039] FIG. 20 is a graphical representation of the Graded Symptom
Checklist total score (along the y-axis) upon serial assessment at
several different scans noted along the x-axis as scan visit for
N=18 eighteen subjects. Flat trajectories appear free from symptoms
while several subjects appear to exhibit symptoms consistent with
concussion.
[0040] FIG. 21 is a graphical representation of the Standard
Assessment of Concussion (SAC) total score (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=18 eighteen subjects. Flat trajectories appear
near 30 (a perfect score) appear cognitively intact while several
subjects appear to exhibit cognitive issues consistent with
concussion.
[0041] FIG. 22 is a graphical representation of the Balance Error
Scoring System (BESS) total error score (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=18 eighteen subjects. Flat trajectories appear
near zero (a perfect score) appear stable within their vestibular
system while several subjects appear to exhibit balance and
vestibular issues consistent with concussion.
[0042] FIG. 23 is a graphical representation of the King-Devick
Ophthalmologic Test (Oride et al 1986) measured in total time
across three test cards (sec) with minimal errors (along the
y-axis) upon serial assessment at several different scans noted
along the x-axis as scan visit for N=18 eighteen subjects. Flat
trajectories near forty seconds appear as consistent and stable
neuro-ophthalmological processing while several subjects appear to
exhibit longer times at early scan visits consistent with
concussion.
[0043] FIG. 24 is a graphical representation of the Graded Symptom
Checklist total score (along the y-axis) upon serial assessment at
several different scans noted along the x-axis as scan visit for
N=18 eighteen subjects. Subjects are paired as concussed (red
traces) or the non-injured team mate (green traces) who followed
the same scan sequence to serve as a control.
[0044] FIG. 25 is a graphical representation of the Standard
Assessment of Concussion (SAC) total score (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=18 eighteen subjects. Subjects are paired as
concussed (red traces) or the non-injured team mate (green traces)
who followed the same scan sequence to serve as a control.
[0045] FIG. 26 is a graphical representation of the Balance Error
Scoring System (BESS) total error score (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=18 eighteen subjects. Subjects are paired as
concussed (red traces) or the non-injured team mate (green traces)
who followed the same scan sequence to serve as a control.
[0046] FIG. 27 is a graphical representation of the King-Devick
Ophthalmologic Test (Oride et al 1986) measured in total time
across three test cards (sec) with minimal errors (along the
y-axis) upon serial assessment at several different scans noted
along the x-axis as scan visit for N=18 eighteen subjects. Subjects
are paired as concussed (red traces) or the non-injured team mate
(green traces) who followed the same scan sequence to serve as a
control.
[0047] FIG. 28 is a graphical representation of the relative beta
brainwave power during an eyes closed task (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=18 eighteen subjects. Subjects are paired as
concussed (red traces) or the non-injured team mate (green traces)
who followed the same scan sequence to serve as a control.
[0048] FIG. 29 is a graphical representation of the Graded Symptom
Checklist total score (along the y-axis) upon serial assessment at
baseline (scan visit 0) and scan visit 1 (along the x-axis) for N=6
six subjects who had a baseline. Concussed athletes are in the left
panel and non-injured teammate controls are in the right panel.
[0049] FIG. 30 is a graphical representation of the Standard
Assessment of Concussion (SAC) total score (along the y-axis) upon
serial assessment at baseline (scan visit 0) and scan visit 1
(along the x-axis) for N=6 six subjects who had a baseline.
Concussed athletes are in the left panel and non-injured teammate
controls are in the right panel.
[0050] FIG. 31 is a graphical representation of the Balance Error
Scoring System (BESS) total error score (along the y-axis) upon
serial assessment at baseline (scan visit 0) and scan visit 1
(along the x-axis) for N=6 six subjects who had a baseline.
Concussed athletes are in the left panel and non-injured teammate
controls are in the right panel.
[0051] FIG. 32 is a graphical representation of the King-Devick
Ophthalmologic Test (Oride et al 1986) measured in total time
across three test cards (sec) with minimal errors (along the
y-axis) upon serial assessment at baseline (scan visit 0) and scan
visit 1 (along the x-axis) for N=6 six subjects who had a baseline.
Concussed athletes are in the left panel and non-injured teammate
controls are in the right panel.
[0052] FIG. 33 is a graphical representation of the relative beta
brainwave power during an eyes closed task (along the y-axis) upon
serial assessment at baseline (scan visit 0) and scan visit 1
(along the x-axis) for N=6 six subjects who had a baseline.
Concussed athletes are in the left panel and non-injured teammate
controls are in the right panel.
[0053] FIG. 34 is a graphical representation of 4 non-injured
control (CTL) subjects whereby the GSC, SAC, BESS, KD time, and
relative beta power (along the y-axis) are each individually
stacked on top of each for each scan visit (along the x-axis). This
is useful in Return To Play decision making.
[0054] FIG. 35 is a graphical representation of 4 concussed (TBI)
subjects whereby the GSC, SAC, BESS, KD time, and relative beta
power (along the y-axis) are each individually stacked on top of
each for each scan visit (along the x-axis). This is useful in
Return To Play decision making.
[0055] FIG. 36 is a graphical representation of 1 non-injured
control (CTL) subject and 1 concussed teammate (TBI) whereby the
GSC, SAC, BESS, KD time, and relative beta power (along the y-axis)
are each individually stacked on top of each for each scan visit
(along the x-axis). This is useful in Return To Play decision
making.
[0056] FIG. 37 is a schematic illustration of laptop or tablet PC.
An external eye tracker is shown below the video monitor and
connects either via wire (e.g. USB) or wirelessly (e.g. Bluetooth,
ZigBee, WiFi).
[0057] FIG. 38 is a graphical representation of the output of a 30
Hz eye tracker when a series of cards are presented which moves the
eye around the corners of the screen from top-left to top-right to
bottom-right to bottom-left to top-left again. The origin for the
coordinate system is the upper left of the computer screen.
[0058] FIG. 39 is a graphical heat map representation of the amount
of time the eyes of study subjects spent focused on the numbers on
the stimuli cards. This data supplements the brainwave, voice and
neuropsychology data.
[0059] FIG. 40 is a graphical heat map representation of the amount
of time the eyes of study subjects spent focused on the numbers on
the stimuli cards. This drawing also illustrates the Areas of
Interest that have been created which enables determination of how
much time was spent within an AOI versus outside various AOIs. This
data supplements the brainwave, voice and neuropsychology data.
[0060] FIG. 41 is a graphical representation of the Graded Symptom
Checklist total score (along the y-axis) upon serial assessment at
several different scans noted along the x-axis as scan visit for
N=40 eighteen subjects. Flat trajectories appear free from symptoms
while several subjects appear to exhibit symptoms consistent with
concussion.
[0061] FIG. 42 is a graphical representation of the Standard
Assessment of Concussion (SAC) total score (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=40 eighteen subjects. Flat trajectories appear
near 30 (a perfect score) and appear cognitively intact while
several subjects appear to exhibit cognitive issues consistent with
concussion.
[0062] FIG. 43 is a graphical representation of the Balance Error
Scoring System (BESS) total error score (along the y-axis) upon
serial assessment at several different scans noted along the x-axis
as scan visit for N=40 eighteen subjects. Flat trajectories appear
near zero (a perfect score) and appear stable within their
vestibular system while several subjects appear to exhibit balance
and vestibular issues consistent with concussion.
[0063] FIG. 44 is a graphical representation of the King-Devick
Ophthalmologic Test (Oride et al 1986) measured in total time
across three test cards (sec) with minimal errors (along the
y-axis) upon serial assessment at several different scans noted
along the x-axis as scan visit for N=40 eighteen subjects. Flat
trajectories near forty seconds appear as consistent and stable
neuro-ophthalmological processing while several subjects appear to
exhibit longer times at early scan visits consistent with
concussion.
[0064] FIG. 45 is a graphical representation of the King-Devick
Ophthalmologic Test (Oride et al 1986) measured in total time
across three test cards (sec) with minimal errors (along the
y-axis) upon serial assessment at several different scans noted
along the x-axis as scan visit for N=40 eighteen subjects shown in
pairs that match a non-injured athlete with an injured athlete.
Flat trajectories near forty seconds appear as consistent and
stable neuro-ophthalmological processing while several subjects
appear to exhibit longer times at early scan visits consistent with
concussion.
DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
[0065] The invention will be described in detail below with
reference to FIGS. 1-45. Those skilled in the art will appreciate
that the description given herein with respect to those figures is
for exemplary purposes only and is not intended in any way to limit
the scope of the invention. All questions regarding the scope of
the invention may be resolved by referring to the appended
claims.
DEFINITIONS
[0066] By "electrode to the scalp" we mean to include, without
limitation, those electrodes requiring gel, dry electrode sensors,
contactless sensors and any other means of measuring the electrical
potential or apparent electrical induced potential by
electromagnetic means.
[0067] By "monitor the brain and nervous system" we mean to
include, without limitation, surveillance of normal health and
aging, the early detection and monitoring of brain dysfunction,
monitoring of brain injury and recovery, monitoring disease onset,
progression and response to therapy, for the discovery and
optimization of treatment and drug therapies, including without
limitation, monitoring investigational compounds and registered
pharmaceutical agents, as well as the monitoring of illegal
substances and their presence or influence on an individual while
driving, playing sports, or engaged in other regulated
behaviors.
[0068] A "medical therapy" as used herein is intended to encompass
any form of therapy with potential medical effect, including,
without limitation, any pharmaceutical agent or treatment,
compounds, biologics, medical device therapy, exercise, biofeedback
or combinations thereof.
[0069] By "EEG data" we mean to include without limitation the raw
time series, any spectral properties determined after Fourier
transformation, any nonlinear properties after non-linear analysis,
any wavelet properties, any summary biometric variables and any
combinations thereof.
[0070] A "sensory and cognitive challenge" as used herein is
intended to encompass any form of sensory stimuli (to the five
senses), cognitive challenges (to the mind), and other challenges
(such as a respiratory CO.sub.2 challenge, virtual reality balance
challenge, hammer to knee reflex challenge, etc.).
[0071] A "sensory and cognitive challenge state" as used herein is
intended to encompass any state of the brain and nervous system
during the exposure to the sensory and cognitive challenge.
[0072] An "electronic system" as used herein is intended to
encompass, without limitation, hardware, software, firmware, analog
circuits, DC-coupled or AC-coupled circuits, digital circuits,
FPGA, ASICS, visual displays, audio transducers, temperature
transducers, olfactory and odor generators, or any combination of
the above.
[0073] By "spectral bands" we mean without limitation the generally
accepted definitions in the standard literature conventions such
that the bands of the PSD are often separated into the Delta band
(f<4 Hz), the Theta band (4<f<7 Hz), the Alpha band
(8<f<12 Hz), the Beta band (12<f<30 Hz), and the Gamma
band (30<f<100 Hz). The exact boundaries of these bands are
subject to some interpretation and are not considered hard and fast
to all practitioners in the field.
[0074] By "calibrating" we mean the process of putting known inputs
into the system and adjusting internal gain, offset or other
adjustable parameters in order to bring the system to a
quantitative state of reproducibility.
[0075] By "conducting quality control" we mean conducting
assessments of the system with known input signals and verifying
that the output of the system is as expected. Moreover, verifying
the output to known input reference signals constitutes a form of
quality control which assures that the system was in good working
order either before or just after a block of data was collected on
a human subject.
[0076] By "biomarker" we mean an objective measure of a biological
or physiological function or process.
[0077] By "biomarker features or metrics" we mean a variable,
biomarker, metric or feature which characterizes some aspect of the
raw underlying time series data. These terms are equivalent for a
biomarker as an objective measure and can be used
interchangeably.
[0078] By "non-invasively" we mean lacking the need to penetrate
the skin or tissue of a human subject.
[0079] By "diagnosis" we mean any one of the multiple intended use
of a diagnostic including to classify subjects in categorical
groups, to aid in the diagnosis when used with other additional
information, to screen at a high level where no a priori reason
exists, to be used as a prognostic marker, to be used as a disease
or injury progression marker, to be used as a treatment response
marker or even as a treatment monitoring endpoint.
[0080] By "electronics module" or "EM" or "reusable electronic
module" or "REM" or "multi-functional biosensor" or "MFB" we mean
an electronics module or device that can be used to record
biological signals from the same subject or multiple subjects at
different times. By the same terms, we also mean a disposable
electronics module that can be used once and thrown away which may
be part of the future as miniaturization becomes more common place
and costs of production are reduced. The electronics module can
have only one sensing function or a multitude (more than one),
where the latter (more than one) is more common. All of these terms
are equivalent and do not limit the scope of the invention.
[0081] By "biosignals" or "bio signals" or "bio-signals" we mean
any direct or indirect biological signal measurement data streams
which either directly derives from the human subject under
assessment or indirectly derives from the human subject.
Non-limiting examples for illustration purposes include EEG
brainwave data recorded either directly from the scalp or
contactless from the scalp, core temperature, physical motion or
balance derived from body worn accelerometers, gyrometers, and
magnetic compasses, the acoustic sound from a microphone to capture
the voice of the individual, the stream of camera images from a
front facing camera, the heart rate, heart rate variability and
arterial oxygen from a would pulse oximeter, the skin conductance
measured along the skin, the cognitive task information recorded as
keyboard strokes, mouse clicks or touch screen events. There are
many other biosignals to be recorded as well.
[0082] By "Return to Play" we mean similar decisions such as return
to duty, return to work, return to learn, return to drive,
insurance coverage decision (return to coverge) or any other return
to activity based decision that has a different context but is
essentially the same question about a human subject trying to
return to an earlier state to resume an "activity" that they
participated in previously.
A System of Multiple Transducers to Both Stimulate and Record
Physiological and Brain Response
[0083] The systems and methods of the invention comprise multiple
transducers to both stimulate and record the physiological response
of the brain and the body in order to assess its health and
function. Central to the system is the ability to directly record
brainwave activity from an electrode place non-invasively on or
near the scalp. Moreover, additional information on brain health
and function can be derived from transducers that measure position
and motion, temperature, cardiovascular properties like heart rate,
heart rate variability, and arterial oxygen, as well as cognitive
information, speech, eye movement, and surface skin conductance to
name a few non-limiting additional biological signal measurement
data stream examples. It is often necessary to bring the system to
the human subject, getting out of the hospital or doctor's office
and enabling data collection in the home or sports field or combat
theater, thus providing accessibility to the brain health and
function assessment from a lightweight and portable form factor.
Moreover, it would be advantageous to have a minimal cost
associated with the system so that it can be used around the globe
to help those in need of brain health and function assessments.
[0084] A solution to these problems includes the creation of a
system of body worn or body proximal electronic modules (EMs or
REMs) with the ability to both record biological signal measurement
data streams as well as present stimuli to the human subject in the
form of various sensory and cognitive challenges and tasks. In
particular, one such electronic module (EM or REM) can be placed in
the vicinity of the head and be either reused over and over if it
does not touch the human body or disposed of if it comes in direct
contact with the human body.
[0085] In one embodiment of the system, as illustrated in FIG. 1, a
human subject 3 is outfitted on their head 4 with an electronic
module or reusable electronic module (REM) 5, which has several
sensors and transducers within it to both stimulate the human
subject and record biological signal measurement data streams ("bio
signals") in a precise fashion driven via software either embedded
within the REM on a local microprocessor control unit (MCU) or
running on a nearby peripheral MCU. In this system, limb 6 in the
form of an arm or limb 7 in the form of a leg can hold additional
REM modules 8 or 10 for additional readout and acquisition of
additional biological signals. As desired, an REM module 9 is
placed on the trunk of the human subject or up by the chest or
around the neck. Nearby or connected via wireless interface, a
peripheral MCU 11 would both control the standardized application
of sensory and cognitive stimuli as well as coordinate the
extensive data acquisition of the biological signals derived from
the human subject. One could envision that the peripheral MCU 11 as
either a laptop, tablet PC or smartphone of today, or perhaps it
may be sitting in separate location altogether from where a human
subject is immersed in an audio-video like home theater of image,
sound, and other sensory stimuli. It is contemplated that the REM
modules could eventually interface with each other via newer RF
technology which enables long distance communication with large
bandwidth. Importantly, peripheral MCU 11 may have database access
either locally via a hard wire 12 to a mass storage device like a
hard drive 13 or, alternatively, it may be connected via a wired or
wireless network interface 14 (e.g. ethernet cable, Wi-Fi, cellular
data modem, satellite data modem to name a few non-limiting
examples) to a remote mass storage device 15 with remote MCU
capability. The purpose of the access to a database is to enable
the system of the present invention to access and pull down
additional information about a human subject from electronic
records that may exist in some other location and where either
downloaded locally to the peripheral MCU 11 or available remotely
through network connectivity 14 to remote data base 15 (for
instance to pull genetic information or other lab results into the
system to make predictive signatures more accurate or precise with
the inclusion of blood type, last recorded blood pressure, or ApoE
genotype status as non-limiting examples). In either case, once a
unique patient identification number has been entered and proper
security clearance made (such as two factor authentication), then
many additional variables of data can be pulled out of the data
base records stored on mass storage device 13 and/or 15.
[0086] Another embodiment of the invention includes a data
recording and analysis system that includes at least one REM placed
on the head of a human subject to record brain related biological
health signals, a peripheral MCU, and a cloud based enterprise
information technology infrastructure to process and report the
data that has been collected. In particular, FIG. 2 illustrates an
electronic REM module 306 on a subject's head transmitting wireless
data to peripheral MCU (in the form of a tablet PC) 304. While the
data is being collected through the Bluetooth port in the MCU, the
camera 300 is recording a movie of images of the subject as they
perform tasks to not only verify their identity but also to analyze
their eye and facial movement for features of interest (including
saccade). Microphone 312 records the voice of the subject for voice
recognition analysis, while built-in accelerometer and gyrometer
302 measure the stability or lack thereof of the subject, while
touch screen 304 of the peripheral MCU records events at precise
times and spatial (x,y) locations on the touch screen. Finally,
when all the various data streams are complete, along with
demographic and personal health information, the entire package of
information is encrypted locally using AES-128 or AES-256 bit
encryption (or equivalent security measures) 308 before being
transmitted at 310 to the virtual or remote based servers through
an internet connection 314 which could be Wi-Fi, Ethernet, cellular
or satellite in nature.
[0087] Once the data is received by the virtual server 320
connections, as shown in FIG. 3, it is decrypted by appropriate
algorithms with the key 322 and then sent on for pre-processing to
identify areas of artifact such as eye blink, drop outs, saturated
rails, movement artifacts, EKG artifacts or other known artifacts
at 324 as described in U.S. Provisional Patent Application No.
61/773,428, filed Mar. 6, 2013. Once the artifacts have been
identified and characterized, the regions of good data for each of
the various data streams are passed through signal processing
software to extract candidate features from each of the data
streams available. In particular, a spectral analysis or FFT module
326 is applied to the data signals, a non-linear dynamics module
328 is applied, as is a wavelet transform module 330. Once each
module has extracted the relevant and candidate features from each
block of data, the software then assembles an extracted biometric
feature table 332 including each of the candidate features from
each of the streams of data, including a listing of the artifact
features as possible diagnostic features as well. From the
biometric feature table 332, predictive analytics 334 are run on
the unknown subject and the predictive models generate an output by
either classifying the subject into one of several groups or
classes or alternatively predicts a regression score as an output.
These information are then compared to either baseline/earlier data
from that same subject or from a demographically match population's
normative data and a report 336 is generated. The report 336 is
then sent electronically to physician 338 who is able to remotely
interpret the report and provide their interpretation before the
report is sent back to the point of care for action by the
healthcare provider who captured the data in the first place.
[0088] It should be pointed out that the artifacts detected in the
pre-processing module could be used themselves as candidate
features to help classify or regress unknown human subject
information according to a verified and validated multi-variate
predictive statistical model as described in U.S. Provisional
Patent Application No. 61/773,428, the disclosure of which is
hereby incorporated herein in its entirety.
[0089] An alternate view is provided by FIG. 4 where active sensor
remote electronic module (REM) 350 is mounted with ear clip 352 on
the human subject's head. The Bluetooth or other local means of
connectivity 354 transfers the data to the peripheral MCU 356
(laptop, tablet or smartphone) whereby the data is encrypted and
sent to the network 358 via internet, cellular or satellite
connectivity. Once at the virtual and remote servers 360, the data
is automatically decrypted and processed 362 at the data processing
center 364 remotely. Once pre-processing, signal analysis, and
predictive modeling are complete, the system automatically 366
generates a report 368. This report is then sent back to the point
of care if requested by an appropriate physician 370 or to an
appropriate physician 370 for interpretation before being sent back
to the point of care to insure that a physician remains a part of
the diagnostic cycle.
[0090] If one examines closely the output from the various sensors
and transducers place on or nearby the human subject, one can see
the quantitative output from each sensor or transducer, after
analog to digital conversion by an ADC into a discrete flow of
digital information. FIG. 5 schematically illustrates the output
from nine sensors and transducers (artificial data created for
illustration purposes only), each labeled as signal 1 through
signal 9. This illustration does not include data from other
biological signal measurement data streams such as the forward
facing image camera, a pulse oximetry, skin conductance
electro-dermal measurements, as a few non-limiting examples of what
is not included. In FIG. 6, each of the generic sensor labels has
been replaced with an example bio signal stream (with the same
artificial data created for illustration purposes only). From the
top of the FIG. 6, one sees the electroencephalogram or EEG in
micro-volts (.mu.V) plotted on the y-axis as a function of time t
along the x-axis. In the second trace down, neuropsychological
cognitive data is illustrated in a plot where discrete response
"events" to computer neuropsychological testing are being captured
as key strokes on a keyboard, mouse clicks with a position (x,y) on
the video monitor's screen or alternatively on a touch screen
display as touch "events" where the location (x,y) much like a
mouse click is recorded as (x,y) spatial pairs at a given time t
(x,y,t). In the next three traces (third, fourth and fifth from the
top) one sees three independent traces from a 3-axis digital
accelerometer or a 3-axis analog accelerometer after passing
through an ADC labelled Ax (g), Ay (g), and Az(g). Acceleration is
often expressed as a fraction or multiple of the gravitational
constant acceleration g=9.8 meters/second. In the sixth trace from
the top (or fourth from the bottom), one can see a microphone
recording trace labelled Voice (mV), typically sampled at either 1
or 2 bytes per sample and from 5 ksam/sec or 8 ksam/sec or 12 or 16
ksam/sec, although many other sampling frequencies are possible. In
the third trace from the bottom labelled Temp(F), the temperature
of the human subject is plotted across time to investigate if any
of the sensory stimulations or cognitive tasks are having an effect
on core body temperature. Lastly, the bottom two traces exemplify
two of three axes of accelerometer data from a second REM labelled
Ax-2 (g) and Ay-2 (g), perhaps located on the trunk at the chest or
small of the back, or on a limb around the wrist or perhaps ankle.
If well registered in time, the multiple streams of biological
signals enable several clever and interesting techniques of data
acquisition and analysis.
Simplified Form Factor for the Acquisition of a Multiple Streams of
Biological Signal Data in the Assessment of Brain Health and
Function
[0091] The systems and methods of the invention comprise device and
equipment form factors that can easily be positioned on the human
body to both stimulate various senses as well as collect a
multitude of bio-signals, can be re-used in part and disposed in
part, and utilized locally using personalized and disposable
materials when they touch the human body. It is often necessary to
insure the integrity and sterility of any item that comes in
contact with a human test subject by either disinfecting the
applied part or dispensing of the previous one and using a fresh
and unused sterile set of materials that come in contact with the
human subject. Moreover, it would be advantageous to have a minimal
cost associated with the disposable parts that get thrown out as
waste into a trash can.
[0092] A solution to these problems includes the creation of one or
more electronic modules ("EM") or reusable electronic modules
("REM") or multi-functional biosensors (MFB) that can be placed on
the body to record bio-signals from the body. In particular, one
such EM module can be placed in the vicinity of the head and be
either reused over and over if it does not touch the human body or
disposed of if it comes in direct contact with the human body.
[0093] In one embodiment as illustrated in FIG. 7, a form factor of
the invention includes a headband 2, which supports an electronic
module or reusable electronic module (REM) 4, which has an active
brainwave sensor 5 that sits directly on the forehead. The
differential input signal is contacted to a non-skull portion of
the body, preferably someplace easy to access like the earlobe or
top of the ear off of the skull through cable 6 to ear clip 7 which
includes either one conductor or two conductors, one for Reference
(REF) and the other for Ground (GND). Alternate off the skull
locations include the neck as mastoid and the nose, in the vicinity
away from the facial skin. The REM 4 and the active brainwave
sensor 5 can be attached through a common medical device electronic
snap or other simple press electro-mechanical connection. The REM 4
and cable 6 can be attached to the headband 2 via Velcro
hook/ladder press closure as well. At the back of the headband, a
piece of Velcro or similar press fit closure 8 can be used to
secure the headband to the human subject's head with a secure but
comfortable tight mechanical fit. In an exemplary embodiment, the
head band 2 is made from Fabrifoam's unique fabric-foam dual layer
material which stretch easily and is very comfortable to sit on the
skin because of their special proprietary water permeation
properties of the material.
[0094] In another alternate embodiment, shown in FIG. 8, headband
80 has REM 83 attached as before but now there are additional
electrodes such as on the temple 81 and or otherwise located around
the head at position 82 and attached to the headband 80. In this
embodiment, two, three or four channels of EEG data can be recorded
to monitor both hemispheres of the brain as well as other spatial
locations. Interconnect cable 85 and ear clip 87 for REF and GND
ear contact are as described before.
[0095] FIG. 9 provides a series of alternative electrode
configurations. FIG. 9A provides a pair of views of alternate
electrode configurations whereby the normally circular electrode is
divided in two hemi-circles or alternatively whereby a normally
square or rectangular electrode is divided in squares or
rectangles. FIG. 9B provides a view of an alternate electrode
configuration whereby the whole conductive electrode has been
sub-divided into 3 equal conductive parts separated by insulator,
either starting from a circle into 120 degree arcs as in FIG. 9A
upper or a rectangle into equal squares FIG. 9B lower. FIG. 9C
provides a view of an alternate electrode configuration whereby the
normally circular (upper) or square electrode (lower) is divided
into 4 equal conducting electrodes depending on geometry and again
divided by insulators to make 4 independent electrodes within the
existing form factor. For instance as a non-limiting example, a
circular electrode divided into four would look like the four
quadrants of FIG. 9C upper, while that of a square divided into
four would look like the array of conducting electrodes shown in
FIG. 9C lower. Thus, if one were to use two independent electrode
clusters, each divided according to one of the illustrations shown
in FIG. 9, then one would be able to deploy a 4 channel (2
locations with 2 electrodes at each location), a 6 channel (2
locations with 3 electrodes at each location) or an 8 channel (2
locations with 4 electrodes at each location) data acquisition
system in the same physical space easily accessible along the area
of the skull under the REM module's support headband with good
mechanical and electrical connectivity.
Trunk Electronic Modules Gather Trunk Data in Addition to the Head
Based REM
[0096] One aspect of the present invention includes the use of
additional electronic modules to collect trunk data, either located
in vicinity of the small of the back, around the chest, or on the
neck, at the same time that the head REM is collecting brain/skull
related biological signal data. For instance, while a human subject
is undergoing a vestibular or balanced based assessment during a
concussion battery of tests, the human subject could be asked to
stand on a firm surface in various postures, consistent with the
Balanced Error Scoring System or BESS (Guskiewicz et al). Rather
than have an athletic trainer or manager subjectively score and
evaluate the human subject for various subjective errors, as is
presently done, a multi-axis accelerometer can measure objective
biological signals of the human subject's stability based on their
head movement and motion while conducting the task and while the
EEG sensor is collecting contemporaneous brainwave data.
[0097] Similarly, accelerometer and/or other position/motion
sensors placed along the trunk, spatially from the neck, to the
chest, to the small of the back enables further objective
measurement of body motion from which to further assess the human
subject's ability to react to change when asked to stand on an
elastic or unstable surface while accelerometers and gyrometers in
the head REM continue to measure brainwaves and head stability
during the task.
[0098] In one embodiment, additional accelerometer data is
collected by a trunk REM attached to the waist or small of the back
while a third REM, attached near the chest or neck, further
quantifies the human subject's balance skills simply,
quantitatively and inexpensively using a 3, 6 or 9 degree of
freedom system at each physical location (head, neck/chest,
waist/back). In addition to conducting these balance related tasks
on a firm surface, use of an inflatable and disposable pillow or
air cushion made from strong plastic provides an inexpensive means
to assess the human subject on a pristine and unused soft and
unstable elastic surface suitable for medical device use. When
reusable foam cushions are permissible, like the Airex model
cushion recommended in the BESS instructions, they are excellent
second surfaces for A versus B comparisons. In instances where
repeated use by multiple human subjects is not permitted, such as
in medical evaluations and assessments, the use of a compact,
disposable, and inexpensive elastic and unstable inflatable pillow
device for a human subject to stand on could advantageously assist
in a concussion or other balance/vestibular system assessment and
is a part of the invention. Here, the same A versus B comparison is
possible, but with the added benefit of a single use disposable
unstable surface, such as the inflatable air pillow.
Incorporation of a Microphone and/or Camera in an REM Module
[0099] In one embodiment, additional data transducers are built
into the REM module such that the system can acquire diverse
streams of bio signal data. One particular embodiment includes the
inclusion of either an acoustic microphone coupled to an analog to
digital converter or the use of a digital microphone which has
essentially the same functionality, just engineered into a single
package for ease of integration into the REM electronics. Typical
digital outputs are in common standards such as RS-232, UART, SPI,
and I2C for local area serial digital communication. An advantage
of the present embodiment is that the control of timing by the
local embedded MCU in the REM is typically tighter and more precise
(sub milliseconds with the ability to approach micro-second timing
precision if not go beyond to sub-microsecond timing precision)
than can typically be achieved with the peripheral MCU, such as an
Apple iPad or Android tablet or Windows Laptop unless one attempts
to run a special "real-time" implementation of those operating
systems, some of which do not yet exist (e.g Apple iOS does not yet
have a real-time OS that the programmer can program control
over).
[0100] In FIG. 10, one can see a rendering of a head based REM
module which is powered by an AAA battery. In the alternative, it
could be powered by coin style batteries for a slimmer and more
compact profile. In addition to a standard "power/pairing" switch
92 and power/pairing indicator LED 94, one can see the
incorporation of an acoustic microphone 96 into the REM design as
well as a forward facing digital image sensor 98 (essentially a
movie camera). The microphone 96 is then capable of picking up
those sounds in the immediate area of the human subject including
the speech of the scan administrator, the speech of the software
narrator, and their own speech (the subject taking the scan), as
non-limiting examples of sounds that would be captured by
microphone 96. Moreover, coughs, sneezes, laughs, falls, etc. would
also be captured in real-time with a tight precision as managed in
hardware by the embedded MCU in a real-time data acquisition
environment.
[0101] The image sensor 98 would be capable of acquiring video
rates or faster of image data. The view of the images would depend
on where the REM is placed on the head and the orientation of the
subject's head. The use of the video images could enable tracking
of the eye at the sample or refresh rate of the sensor, typically
30 frames per second or 60 fields per second of a standard
interlaced NTSC video device. That said, spatial sub-sampling of a
sub-region of interest of a CCD pixel array can greatly accelerate
the full frame or field rate to enable 60, 80 even 100 Hz sample
rate of a smaller field of view which could advantageously be
focused on the eyes for the analysis of saccade to distractions or
other neuropsychological tests which have been published within the
scientific literature extensively. Of course, either microphone 96
or image sensor 98 could be utilized alone or in combination in
various REM modules, depending on the particular circumstances.
Use of Google Goggles or Other Eye Tracking Devices to Monitor Eye
Movement
[0102] In a recent advance, Google has come out with an
eyeglass-like device which can project images and track the eye to
move a camera to where one is looking. This sort of technology
could be incorporated into an REM or the electronics of the REM
could be incorporated into a Google Glass like device to combine
the eye tracking capability with the other biological sensor data
streams. This could be especially useful when one wants to assess
the quality of the neuro opthalmologic tracking of the eye by the
brain. Visual Saccade as designed into the Peirce test, King-Devick
test, Developmental Eye Movement test, or oddball or mismatch
saccade tests are well known to provide meaningful streams of eye
gaze information. This system would not compete with high end 128
or 256 sam/sec systems built into goggles and other form factors
that are dedicated to this task, rather this would represent one
more bio signal data stream that could be analyzed alone and then
in conjunction with the other data streams.
[0103] In FIG. 11, one can see a block rendering of a side shot of
the Google Glass device. Surround member 114 is essentially the
piece which wraps around the head from ear to ear and from which
all other pieces are supported. The pair of nose pads 118 and 120
supports the device on the bridge of the nose much like eye
glasses. Electronics module 116 hangs below and encloses video
camera 112 and 9-axis Motion Sensing Unit 117 (Invensense 9650
which includes a 3 axis accelerometer, 3 axis gyrometer and 3 axis
electronic compass). The Glass screen and possible eye track
reflector/sensor 110 (drawn with dotted lines not solid since
transparent in real life) sits to the right in the field of view.
The Eye tracking sensor or system 111 within the Glass device could
be used in the present invention as one more element of a biosensor
data stream, to monitor the position of the eye or eyes, especially
during neuro ophthalmologic saccade based visual tasks such as the
Pierrce Saccade, King-Devick Test, Developmental Eye Movement (DEM)
or proprietary improvements thereof.
[0104] The Motion Sensing Unit MSU 117 integrated into the Glass
could be used in the present invention as one more element of a
biosensor data stream, in particular when one is conducting static
balance tasks, such as the various postures of the BESS, or dynamic
balance tasks, such as the "stand, walk and turn" task. These
additional biosensors would need to be integrated into the overall
multi-modal system by streaming data via wired or wireless
connectivity to an MCU either embedded into an REM as described
elsewhere herein, or alternatively, the electronics module 116 of
FIG. 11 could house the electronics for the head REM, serving as
the MCU with attachable electrodes placed on the forehead with
adhesive to record the brainwave EEG bio signal data stream.
Bluetooth, ANT, Zigbee, WiFi are all local area wireless
connectivity options, as well as direct wire options using
miniature connectors such as USB micro or smaller.
[0105] It is also contemplated that the data may reside on a
removable SD card in an electronics system or REM and not be
transmitted wirelessly, but rather stored locally to a removable
mass storage device like an SD card. This alternative has the
advantage of not requiring wireless connectivity but gives up the
ability to monitor in real time the data streams and the
synchronization from interaction with the stimuli. Each "Use Case"
is often different so it may be advantageous to have local SD card
storage in some instances and not in others. In one non-limiting
example, it may be advantageous to have local storage if one wants
to monitor a patient for possible seizures over a 24 to 48 hour
period of ambulatory biosensor monitoring. Thus in this strictly
passive monitoring application, stimulation or probe presentation
is not as important, so use of a peripheral MCU like a tablet or
smartphone may not be necessary.
Embodiments Around Activated Patient Sensory and Cognitive
Stimulation
[0106] Application of sensory stimulants to the patient allows more
focused and detailed evaluation of multiple modes of biological
signal data streams. Multi-modal data can be acquired by measuring
EEG signals at the same time that accelerometer based signals,
temperature signals, pulse oximetry signals, eye gaze signals and
other biological signals are being simultaneously acquired before,
during and/or after a patent's response to a sensory stimulant or
cognitive challenge.
Photic Stimulation
[0107] Visual stimulants such as photic stimulation while a
subject's eyes are closed or via the presentation of certain types
of affective photographic images can be utilized either
independently or via the data capture microprocessor device (MCU)
(computer, tablet PC, cell phone, or other dedicated custom device
with microprocessor and wireless connectivity) used to collect the
wireless bio-signal data from the various REM units on the head,
neck/chest, waist/back, hand/wrist or foot/ankle. In one particular
embodiment, the Google Glass display is used to stimulate the right
eye with photic stimulation of various spatial and temporal
frequency in contrast to the stimulation possible to the asymmetric
left eye where no Glass display is located. This asymmetry can be
leveraged to conveniently both stimulate and record the brain of
the subject from a Google Glass.
[0108] In one particular embodiment of the present invention, as
shown in FIG. 12, an isolated LED 122, a pair of LEDs 126, a triple
combination of LEDs 130 or an LED array 128 can be mounted on the
front of an head REM module 124 and directed forward from the
forehead or angled downward slightly so that a mirror or glass
surface from a video monitor can reflect the light output from the
LED back towards the eyes when they are closed for photic
stimulation. The advantage of this sort of approach, over utilizing
the video monitor in the peripheral MCU (e.g. laptop, tablet PC, or
smartphone), is that the dedicated LED drivers can be housed within
the REM which enables orders of magnitude more precise temporal
response of the LED(s) than is typically possible from a peripheral
MCU operating system (MS Windows, Apple iOS or Google Android). A
non-real time OS is generally not be compared to an embedded
real-time controller, which exhibit measured jitter in the
sub-millisecond range, sometime even the micro-second range,
instead of the 10-50 millisecond latency range, typical of
Microsoft Windows, Apple's iOS or Google's Android non-real-time
operating systems.
[0109] Also, by use of three primary color LEDs (a Red, a Green,
and a Blue LED), one can make color combinations that slide across
nearly all colors of the rainbow spectrum, enabling the choice of
color stimulation of the light by mixing appropriately the LED
outputs to make the rainbow of colors of the electromagnetic
spectrum. Importantly, white light can be created from the
superposition of all three wavelengths of light in equal amplitude.
This would advantageously enable the embedded software to control
the REM MCU via the Bluetooth link and control the LED output with
a real-time embedded processor or something with much shorter
latency than the Windows, Apple or Google operating systems
mentioned above.
Visual Stimulation
[0110] In one particular embodiment of the invention, photographic
images are presented which have desirable emotional and reactive
properties. In one embodiment, the photographic images have been
artificially manipulated in software like Adobe Photoshop in an
interesting fashion. They are then presented as a sequence of
images to assess the mood or emotional response qualities of an
individual under assessment. For instance, an image of a pig can be
altered so as to add wings and then be superimposed above a wavy
ocean surface. In this fashion, the subject under assessment would
see a "flying pig" presented which would typically result in a
smile from a healthy normal individual as we normally do not see
pigs fly. Alternatively, when someone is less affective, displays a
mood or emotional dysfunction, imbalance, or disorder, perhaps is
suffering from a concussion or mild traumatic brain injury, the
subject may not react in the normal or normative manner. This
altered reaction to the photographic image can be biologically
characterized, measured, monitored and observed through the various
biological signal data streams from the various sensors within the
head REM module or peripheral REM modules. In particular, use of
galvanic skin conductance is an excellent means to assess emotional
response as this biosensor measures the skin conductivity which
changes when anxiety (in the form of sweat or skin perspiration),
fear (again sweat or perspiration) and other emotional states of an
individual.
[0111] Thus, in this fashion, a sequence of images from a short
stack of photos, for instance like N=4 images, to a long stack of
photos, like N=30 images, can be presented to a subject with a set
frequency (e.g. 0.1 Hz or 0.05 Hz) or time delay between transition
of images on the video monitor (e.g. display each for 15 seconds in
one instance or for 3 seconds in another instance).
[0112] As an alternate embodiment, the International Affective
Picture System (IAPS) can be utilized. The International Affective
Picture System (IAPS) is being developed to provide a set of
normative emotional stimuli for experimental investigations of
emotion and attention. The goal is to develop a large set of
standardized, emotionally-evocative, internationally-accessible,
color photographs that includes contents across a wide range of
semantic categories. The IAPS (pronounced eye-aps) is being
developed and distributed by the Center for Emotion and Attention
(CSEA) at the University of Florida which has already calibrated
photograph images with various valences can be utilized to provide
a calibrate stimulation from which one can quantitate the
characteristics and biometric response of the human subject under
assessment. Reference: Lang, P. J., Bradley, M. M., & Cuthbert,
B. N. (2008). International affective picture system (IAPS):
Affective ratings of pictures and instruction manual. Technical
Report A-8. University of Florida, Gainesville, Fla.
Auditory Stimulation
[0113] Sensory stimulants such as sound also may be provided either
independently or with the sound card within the data capture
microprocessor device (MCU) (computer, tablet PC, cell phone, or
other dedicated custom device with microprocessor and wireless
connectivity) used to collect the wireless bio-signal data from the
REM. Sound events are triggered via the speaker or sound card on
the computer at various times for the patient to respond to both
instructions as well as auditory stimulations of a novel nature as
described elsewhere. This may be through the speakers as well as
through ear buds or other personalized listening devices.
[0114] As an alternate embodiment, the International Affective
Digitized Sound system (TADS) can be utilized. The International
Affective Digitized Sound system (IADS) provides a set of acoustic
emotional stimuli for experimental investigations of emotion and
attention. This set of standardized, emotionally-evocative,
internationally accessible sound stimuli includes contents across a
wide range of semantic categories. The IADS (pronounced "eye-ads")
is being developed and distributed by the Center for Emotion and
Attention (CSEA) at the University of Florida. The calibrated
sounds can be utilized to provide a calibrated stimulation from
which one can quantitate the characteristics and biometric response
of the human subject under assessment. Reference: Bradley, M. M.,
& Lang, P. J. (1999). International affective digitized sounds
(IADS): Stimuli, instruction manual and affective ratings (Tech.
Rep. No. B-2). Gainesville, Fla.: The Center for Research in
Psychophysiology, University of Florida.
Gastronomic Stimulation of Taste and the Gastrointestinal Tract
[0115] In addition to visual and auditory sensory stimulates,
gastronomic or tongue based stimulation is also possible with the
present invention. In one non-limiting embodiment, shown as FIG.
13, a non-invasive electronic tongue stimulator of the cranial or
other nerve is used to activate the brain. The device is powered by
a battery within electronics housing 140. Switches enable the
device to be turned on at 152 or off at 144 while other buttons
increase 154 or decrease 142 the power or intensity of the
electronic tongue stimulation. A connecting member 146 transfers
signals from the electronics within electronics housing 140 to the
mouth piece stimulator 148 that sit directly against the tongue.
Electrodes 150 are small concentric circles of electrode designed
to directly couple with nerve endings on the tongue. In FIG. 14,
one can see a closer view of the tongue activating surface 162
connected structurally and electrically by connecting member 160.
The individual electrodes 170 which couple with the tongue directly
are drawn as round electrodes with solid lines of insulator.
Alignment posts 164, 166, and 168 are used to align disposable
conducting plates to transfer charge to the subject but can be
thrown away after a single use. FIG. 15 shows such a disposable
sheath 180 which includes a matched plate or surface of conducting
electrodes. This grid is aligned with the grid on the device by the
alignment posts or fixtures 182 and 184 (the third post is not
labeled in this figure).
[0116] An example real world device like this is called the PoNS
Device, developed by the University Wisconsin, Tactile
Communication & Neurorehabilitation Laboratory (TCNL)). The
PoNS is a battery-powered device and is placed in the mouth where
thousands of nerve endings on the tongue can send messages to the
healthy areas of the brain. The idea is that the stimulation, in
combination with therapeutic exercise, helps the brain form new
neural pathways for recovering functions like balance and movement.
Those skills are vital for those affected by MS, cerebral palsy,
traumatic brain injuries, strokes and Parkinson's disease. In the
present invention, the PoNS device can be used to stimulate the
brain through the neural response of the tongue, rather than
through auditory stimulation, visual stimulation, balance based
stability tasks, cognitive tasks as described earlier. The response
across the various biological signal measurement data streams can
be quantitatively and accurately acquired. Once acquired, the new
signals can be analyzed and compared to either earlier measurements
within the same subject or to population or other such norms
created as reference values. Notice that by use of the PoNS device
or other tongue based electrical stimulator designed for brain
health assessment, direct assessment of the tongue's neural
connections to the brain is possible without the use of food and in
a more reproducible and quantitative fashion.
[0117] The PoNS device or other tongue based electrical stimulator
can be controlled by the peripheral MCU via wireless means with a
Bluetooth radio or other RF connectivity means (ZigBee, ANT, Wi-Fi,
proprietary) directly or through bi-directional communication with
the head REM module which could then subsequently control the PoNS
or other electrical tongue stimulator from software embedded within
the head REM module's (or any other REM module's) local MCU (such
as TI MSP430 16-bit microprocessor or any of the various ARM Cortex
M-series microprocessors like the ARM Cortex M3 or M6 or M8). In an
implementation where the embedded software in an REM module
control's the signaling to the neural tongue stimulator, the
precision and timing compared to traditional non-real time
operating systems will be considerably better for all the same
reasons described earlier.
Olfactory Simulation
[0118] A means of olfactory stimulation could be using an UPSIT
card or cards from Sensonics where UP SIT stands for the University
of Penn Smell Identification Test (UPSIT) to provide olfactory
stimulation to the nose of an individual at pre-defined times
indicated by the instructions provided by the peripheral MCU
software. This could include manually scratching and sniffing each
of any number of cards with odors as prescribed and directed. The
results are automatically recorded by the various multi-modal
biological sensor data streams being generated from the human
subject under assessment at that time.
[0119] In a more automated fashion, olfactory based stimulation is
also possible with the present invention. In one non-limiting
embodiment, shown as FIG. 16, a non-invasive electronic nose or
olfactory bulb stimulator is used to activate the brain. The device
is powered by a battery within electronics housing 198. Switches
enable the device to be turned on at 212 or off at 202 while other
buttons increase 214 or decrease 200 the power or intensity of the
electronic nose stimulation. A connecting member 206 transfers
signals from the electronics within electronics housing 198 via
connector 204 to the thin and flexible nose piece stimulators 208
that sit directly against the receptors of the olfactory bulb.
Electrodes 210 are small concentric circles of electrode designed
to directly couple with nerve endings on the olfactory bulb. In
FIG. 17, one can see a closer view of the nose activating surface
226 connected structurally and electrically by connecting member
220 and nostril support 222. The individual electrodes of 226 which
directly couple with the receptors of the olfactory bulb are drawn
as round electrodes with solid lines of insulator. Alignment posts
228 are used to align flexible and disposable conducting grids used
to transfer charge to the subject but can be incorporated into
disposable sheaths 224 (long enough to keep the reusable device
away from touching the human subject) which can be thrown away
after a single use.
Transcranial Pulsed Current Simulation as a Neuro Diagnostic
Procedure
[0120] Another embodiment of the present invention is the means of
stimulating the brain with cranial stimulation. One such commercial
device, the Fisher Wallace Cranial Brain Stimulator provides
micro-currents of electricity to aid those with issues of insomnia,
anxiety, depression and pain. This device and approach can be used
to stimulate the brain and we can measure the response of the brain
due to the cranial stimulation. For instance as a non-limiting
example, one can scan the subject in a battery of tasks with the
system, equipment and methods of the present invention before they
receive cranial stimulation from a Carter Wallace or equivalent
brain stimulator, and then after the 20 minute therapeutic
treatment, the human subject can be re-scanned and the response
measured due to the cranial stimulation. Based on this response
signature, biomarker differences can be derived for both healthy
normal as well as disease, injury, or disorder cohorts. Signatures
derived from this dual scan approach can be used diagnostically for
any of the various intended uses, as "diagnostically" can mean as
many as ten different intended uses as earlier defined.
[0121] Particular embodiments of this approach include the use of
the cranial stimulator to diagnostically assess for
concussion/traumatic brain injury, migraines, mild cognitive
impairment and dementias, pre-motor Parkinson's disease, as well as
various neuropsychiatric conditions such as depression, bipolar,
schizophrenia, anxiety or panic disorder, post-traumatic stress
disorder. Additionally, it is contemplated that this approach could
have diagnostic utility in the diagnosis of mental disorders of the
brain, including multiple personality disorder, dyslexia,
hallucinations, phobias, addictions, alcohol abuse, eating
disorders such as anorexia or bulemia, obsessive-compulsive
disorder, and mood disorders.
Transdermal Pulsed Current Simulation of the Peripheral Nervous
System as a Neuro Diagnostic Procedure
[0122] Furthermore, the present invention contemplates use of
transdermal pulsed current stimulation as well, in the form of
peripheral stimulation such as TENS units, as this could have an
important diagnostic impact on who may have peripheral nervous
system issues in addition to diagnosis of central nervous system
issues. The results are automatically recorded by the various
multi-modal biological sensor data streams being generated from the
human subject under assessment at that time.
[0123] In one particular embodiment of the invention, a TENS unit
is attached to the left and right finger pads which are known to
have a lot of nerve endings and stimulated in a characteristic
fashion. Brain related response, synchronized to the peripheral
stimulation, in the form of an EEG brainwave sensor, galvanic skin
conductance, pulse oximetry, cerebral blood flow, temperature and
other biosignal data streams would be collected. If the TENS
stimulation had cyclic activity in time, then a locked-in signal
could be investigated and look for phase lag between the peripheral
TENS stimulus and the biosensor responses.
Use of the Multi-Modal System to Create Multi-Modal Signatures for
Disease or Injury
[0124] Using the system of the invention, one can build extracted
biometric tables that include features extracted from multiple
modes of biological signal data. As a non-limiting example, two
groups of subjects, group A who experienced a concussion (mTBI) or
mild traumatic brain injury, and group B who did not and serve as
Controls (CTL), were recruited under the supervision of an
Institutional Review Board. Participants from both groups A and B
were scanned identically with an electronic REM module including a
single electrode EEG. A 5 minute protocol was implemented including
30 seconds Eyes Closed, 30 seconds Eyes Open, conducting the
King-Devick test for approximately 3 minutes and then 30 seconds
Eyes Closed, 30 seconds Eyes Open again. The stop watch times and
errors for each card of the King-Devick test were recorded manually
by the test administrator while the peripheral MCU (a laptop
computer) presented the cards and recorded the responses of the
individuals via the microphone. The data was blinded to participant
for the purposes of artifact detection, signal processing and
feature extraction. The extracted feature data table was then
quality controlled and scrubbed to remove as many errors as
possible. The total time for the King-Devick test was created as
one extracted variable and underwent a logistic classification
model. The result of this model indicated that the King-Devick time
alone predicted the classification of the individuals approximately
62% of the time. Independently, the relative power in each of the
delta, theta, alpha, beta and gamma bands was analyzed in a
logistic classification model where the EEG feature was the
predictor x-variable and the clinical outcome (grp A or B) was the
outcome or dependent y-variable in the predictive analytic model.
The analysis was conducted in JMP Pro v10 from SAS (Cary,
N.C.).
[0125] FIG. 18 illustrates the logistic plot 420 for the
relative-beta power (from 12-30 Hz) showing a decreased relative
beta power in the concussed group A relative to control group B.
When one constructs the receiver operating characteristic (ROC)
curve 430, one can see that the EEG feature alone predicts with
accuracy approximately 65% of the time as defined by the summary
ROC Area Under the Curve (AUC) statistic.
[0126] FIG. 19 illustrates in ROC plot 440 that the area under the
curve (AUC) is now 70% when the King-Devick final test time in
seconds (a cognitive measure of the subjects brain) is combined
with the relative beta EEG power (a brainwave measure), creating a
multi-modal signature. When one adds the co-variates of age and
gender, the AUC raises to 76% as shown in ROC plot 450, fully
corroborating the system and methods of the invention. As one adds
additional modalities of information, from either the
accelerometers, the microphone from voice analysis, from the camera
for image analysis or eye tracking, one can anticipate that the
accuracy of the predictive model will increase further as it aids
healthcare providers in the diagnosis of a given condition. This
exemplifies the power of a multi-modal system of objective
biosensors to assess brain health and function.
Use of Correlation Analysis Across Time Series in the Multi-Modal
Bio Signal Data Streams
[0127] The present invention explicitly contemplates the use of two
point, three point or higher order correlations in time to examine
interactions between the various bio-sensor data streams. For
instance, one could look at the time series of samples from a
microphone sampled at 8 KHz and the EEG from a single lead sensor
sampled at 512 Hz and look at any of the various two point
correlation functions available in the literature or MATLAB tool
boxes. Note that one can play with both spatial variables as
biosensors can be spatially in different locations or temporally
where the variable data streams are occurring either in real-time
simultaneously or with a defined or calculated lag in time between
variables of interest (so called phase shift by some). In addition,
techniques such as spatial coherence and concordance can be used
either between two sensors of the same modality (which is typically
done for EEG) but similar approaches can be adapted to the multiple
but different modality streams of bio signal data from the system
of the present invention.
[0128] As CPU processing power increases into smaller form factors,
one can envision the real-time processing of multiple biological
signal data streams through embedded digital signal processors
(DSPs) and other high end MCU devices embedded within the head REM
or trunk located REM or extremity located REM modules.
Use of an Infra-Red Eye Tracker During Neuro-Opthalmologic
Tasks
[0129] As an alternate approach to a Google Glass eye tracker, one
could employ other dedicated hardware such as from Tobii, GazePoint
or other eye tracker manufactures which stream left and right eye
position and pupil diameter measurements continuously. From the
output eye gaze position, one can make measurements of fixation on
various objects in a stimuli field of view, as well as saccades or
anti-saccades which are of interest. Stimulation visuals could
include instructions, static photographs or artistic creations,
movies, web pages, advertisements, pdf documents, etc. Predefined
areas of interest (AOI) can be created and the eye gaze data
superimposed on top of the areas of interest to define metrics of
fixation and saccade relative to the AOI's. Candidate metrics can
be extracted from the eye gaze data to include time to first
fixation, fixation duration, total fixation duration, visit
duration, total visit duration, percentage fixated, saccade
accuracy, anti-saccade accuracy to name some non-limiting examples
of features extracted from the raw eye gaze data streams. These
extracted features can then be incorporated into summary feature
tables of the present invention and used to construct multi-variate
signatures and classifiers along the with extracted brainwave
features, speech recognition features, neuropsychological test
data, accelerometer based balance measures, etc.
EXAMPLES
[0130] While the above description contains many specifics, these
specifics should not be construed as limitations on the scope of
the invention, but merely as exemplifications of the disclosed
embodiments. Those skilled in the art will envision many other
possible variations that are within the scope of the invention. The
following examples will be helpful to enable one skilled in the art
to make, use, and practice the invention.
Example 1
TIRHR Concussion Study
[0131] In collaboration with an non-profit mountain based medical
institute near Lake Tahoe, two groups of subjects were enrolled in
an Institutional Review Board approved clinical protocol, wherein
the first group of subjects (group A) were clinically diagnosed
with a concussion (mTBI) or mild traumatic brain injury and second
control cohort of subjects (group B) were enrolled who did not have
any issue with concussion and served as Controls (CTL) were
recruited under the supervision of an Institutional Review Board.
Participants from both groups A and B were scanned identically with
an electronic REM module including a single electrode EEG device as
described in PCT Patent Application PCT/US2012/046723, filed Jul.
13, 2012. The 5 minute scan protocol included 30 seconds Eyes
Closed, 30 seconds Eyes Open, approximately 3 minutes to conduct
the King-Devick test and then closed with a 30 seconds Eyes Closed,
30 seconds Eyes Open block again. The stop watch times and errors
for each card of the King-Devick test were recorded manually by the
test administrators while the peripheral MCU (a laptop computer)
presented the cards and recorded the responses of the individuals
via the microphone. The head based REM module continuously recorded
the forehead EEG from position Fp1 relative to mastoid on the ear
for reference REF and ground GND. The data was encrypted locally
before being transported over a secure pipe to a virtual server in
cyberspace.
[0132] Signal analysis scientists were blinded to participant
clinical diagnosis for the purposes of artifact detection, signal
processing and feature extraction. The extracted feature data table
was then quality controlled and scrubbed to remove as many errors
as possible. The total time for the King-Devick test was calculated
according to the published procedure of using the minimal number of
errors and then summing the individual times to read all three
cards in succession. This total time represents one extracted
variable and underwent a logistic classification model. The result
of this model indicated that the King-Devick total time in seconds
alone predicted the classification of the individuals approximately
62% of the time (AUC=0.62).
[0133] Independently, analysis for the parallel data stream of EEG
brainwave information, sampled at 128 samples per second with
10-bits of amplitude resolution was then Fourier transformed to
determine the spectral properties. The relative power in each of
the delta, theta, alpha, beta and gamma bands was analyzed in a
logistic classification model where the EEG feature was the
predictor x-variable and the clinical outcome (grp A or B) was the
outcome y-variable in the model. The analysis was conducted in JMP
Pro v10 from SAS (Cary, N.C.).
[0134] In FIG. 18, one can see the logistic plot 420 for the
relative-beta power (from 12-30 Hz) showing a decreased relative
beta power in the concussed group A relative to control group B.
When one constructs the receiver operating characteristic (ROC)
curve 430, one can see that the EEG feature alone predicts with
accuracy approximately 65% of the time as defined by the summary
AUC statistic. In FIG. 19, one can see in ROC plot 440 that the
area under the curve (AUC) is now 70% when the King-Devick test
time (a cognitive measure of the subjects brain) is combined with
the relative beta EEG power (a brainwave measure), creating a
multi-modal signature. When one adds the co-variates of age and
gender, the AUC raises to 76% as shown in ROC plot 450, fully
corroborating the system and methods of the invention. As one adds
additional modalities of information, from either the
accelerometers, the microphone from voice analysis, from the camera
for image analysis, one can anticipate that the accuracy of the
predictive model will increase further as it aids healthcare
providers in the diagnosis of a given condition. This exemplifies
the power of a multi-modal system to assess brain health and
function.
Example 2
Lehigh University Sports Medicine Concussion Study
[0135] In collaboration with an NCAA Division 1 university, several
groups of subjects were enrolled in an Institutional Review Board
approved clinical protocol, wherein the first group of subjects
(group A) were clinically diagnosed with a concussion (mTBI) or
mild traumatic brain injury, a second control cohort of subjects
(group B) were enrolled who did not have any issue with concussion
and served as non-injured Control ssubjects (CTL), while other
athletes from other sports (Group C, etc.) were recruited under the
supervision of an Institutional Review Board as well. Participants
from groups A, B, C and others were scanned identically with an
electronic REM module including a single electrode EEG device as
described in PCT Patent Application No. PCT/US2012/046723, filed
Jul. 13, 2012. The 22-24 minute scan protocol included 1 minute of
Eyes Closed, 1 minute of Eyes Open, an automated application of the
Graded Symptom Checklist from the SCAT-2, elements of the Standard
Assessment of Concussion (SAC) including orientation, immediate
memory recall, concentration, delay memory recall, a full Balance
Error Scoring System (on both firm and foam surfaces), King-Devick
Test Cards, binaural beat audio stimulation at 6 and 12 hertz beat
frequency centered at 400 Hz, photic stimulation, and a fixation
task including a moving red cross for 1 minute.
[0136] The stop watch times and errors for each card of the
King-Devick test were recorded manually by the test administrators
while the peripheral MCU (a Dell Vostro 3550 laptop computer)
presented the cards and recorded the responses of the individuals
via the microphone and mouse clicks. The BESS errors were recorded
manually as well as the SAC responses. The head based REM module
continuously recorded the forehead EEG from 10-20 montage position
Fp1 relative to mastoid on the ear for reference REF and ground
GND. A multi-modal assessment consisting of an EEG data stream, a
cognitive data stream (reaction time and accuracy), self-report of
concussion symptoms, and a microphone data stream were recorded
depending upon which tasks were being conducted. The data was
encrypted locally before being transported over a secure connection
pipe to a secure virtual server in cyberspace.
[0137] Signal analysis scientists were blinded to participant
clinical diagnosis for the purposes of artifact detection, signal
processing and feature extraction. The extracted feature data table
was then quality controlled and scrubbed to remove as many errors
as possible. The total time for the King-Devick test was calculated
according to the published procedure of using the minimal number of
errors and then summing the individual times to read all three
cards in succession. This total time represents one extracted
variable and underwent a logistic classification model. Serial
assessments were conducted on both concussed athletes and controls
with from three to up to ten scans assessing both concussed and
controls.
[0138] As can be seen in FIG. 20 and FIG. 41 for the total scores
of the Graded Symptom Checklist, some subjects appear flat or
normal in their symptoms, while others (such as subject S16 in FIG.
20) show dramatically elevated levels of symptoms consistent with
concussion, which resolve in time back to no symptoms. FIG. 21 and
FIG. 42 shows the total score from the Standard Assessment of
Concussion (SAC) with a maximum healthy value of 30 points plotted
along the y-axis across time measured in several different scan
visits along the x-axis. Flat trajectories which appear near 30 (a
perfect score) appear cognitively intact (such as subject S03 of
FIG. 20) while several subjects (such as subject S07 of FIG. 20)
appear to exhibit cognitive issues consistent with concussion which
resolve in later scan visits. In FIG. 22 and FIG. 43, the Balance
Error Scoring System (BESS) total error score (summed across all
three stances on both a firm and foam surface) is plotted in time
across scan visits (which are not necessarily at equal intervals of
time between them in days. One can see that flat trajectories which
are observed near zero (a perfect score) appear relatively stable
within their vestibular system while several subjects appear to
exhibit balance and vestibular issues consistent with concussion,
shown as an elevated number of errors which decline with meaningful
slope over time till they plateau within some fluctuations around
normal performance.
[0139] The final slice of this data can be seen in FIG. 23 and FIG.
44, where the King-Devick Ophthalmologic Test (Oride et al 1986)
total time, summed across three test cards in seconds is plotted
vertically across scan visit on the longitudinal or x-axis. Flat
trajectories that hover around a minimum value (typically forty
seconds) appear consistent and stable in their
neuro-ophthalmological processing, a typically represent healthy
non-injured controls subjects, while several subjects (such as S01
and S12 in FIG. 23) appear to take longer times at early scan
visits which then relax down to a stable and consistent amount of
time, consistent with a concussion phenotype where the brain injury
resolves itself over days to weeks and a baseline level of
performance re-emerges. FIG. 45 provides a pair-wise view of the
same data in FIG. 44 where the concussed subject and their
non-injured teammate control comparator subject are plotted
together.
[0140] It should be clear from the previous four sets of data in
FIG. 20 thru FIG. 23 and FIG. 41 thru FIG. 44, that one can combine
a symptom data stream, a cognition data stream, a
balance/vestibular data stream and a neuro-ophthalmologic data
stream into a multi-variate composite consistent with the present
invention. Moreover, cross correlation and predictive models can be
built from this and other bio signal data streams including the EEG
data stream and the microphone data stream, not yet included in the
analysis shown.
[0141] Further analysis by pairing the concussed and non-injured
control subjects together can reveal interesting information as
shown in FIG. 24 through FIG. 27, which are the same four metrics
just plotted pairwise on the plot for both the concussed athlete
and their non-injured comparator teammate control. Interestingly,
FIG. 28 shows the relative beta power in 9 pairs of athletes, with
the concussed athlete in red and the non-injured teammate control
in green. The results appear mixed as some subjects exhibit the
literature reported lowering of relative beta in TBI (such as the A
pair or the E pair or the G pair). Moreover, analysis of baseline
adjusted first scans after a "putative event" can help aid in the
assessment of a putative concussion in a human subject as shown in
FIG. 29 through FIG. 33.
[0142] For instance in FIG. 29, in seems clear in the limited
sample that the elevated GSC above 5 at visit 1 is distinct for
concussed subjects and not for controls. Thus, the very limited
data supports a predictive biomarker of GSC.sub.totai
(Visit1)-GSC.sub.total (baseline=visit0)>5 as "likely
concussed." However, upon review of the additional data from FIG.
41, one can conduct an item analysis of each question within the
GSC and learn that the top most important elements or questions
within the GSC (from most important to least important) are 1) "Do
you have a Headache", 2) the total or GSC-Sum, 3) "Don't Feel
right", 4) "slowed down", 5) "In a Fog", 6) "Pressure in the head",
7) "Dizzy", 8) "Difficulty Concentrating", 9) "Fatigue", 10)
"Drowsy", 11) "Sensitivity to Light". Thus if one wanted to shorten
the GSC in time and pair down the number of questions but not
reduce the discriminatory power, one could construct a shortened
"GSC-short" consisting of the top 8, 9, 10 or 11 items from the 18
element GSC.
[0143] From FIG. 30, the concussed athletes are not showing a
distinct change from baseline for the standard assessment of
concussion in its totality. However if one analyzes the individual
components of the SAC, one sees that the most important SAC
elements include (from most to least important) the Delayed Memory,
Concentration, SAC-total score, Immediate memory, Orientation.
Thus, if one wanted to shorten the SAC while maintaining diagnostic
discriminatory power, one could include in a shortened SAC only the
Delayed memory and Immediate memory elements of the SAC, or
alternatively include the Concentration component as well. The
Orientation element does not appear to confer much discriminatory
power.
[0144] From FIG. 31, it appears that the BESS total error score is
a variable that does not appear to be reliable in such a small
sample of human subjects. Additional data is now available from
that shown in FIG. 43, which supports the earlier perspective. On
the other hand, if one investigates each of the six elements of the
BESS (from most to least important), one fines that the elements
sort as: BESS-TandemStance-FirmSurface,
BESS-TandemStance-FoamSurface, BESS-TotalErrors,
BESS-SingleFoot-FoamSurface, BESS-SingleFoot-FirmSurface,
BESS-DoubleStance-FoamSurface, and lastly
BESS-DoubleStance-FirmSurface. Thus, if one uses just the foam, it
reduces the task by 50% yet appears it will remain helpful.
[0145] From FIG. 32, the total time for the K-D task appears quite
variable as well with limited data; however, if one includes the
results from FIG. 44, it appears clear that saccade based card
tasks are an important means to differentiate.
[0146] Moreover, in FIG. 34, a montage of four non-injured control
subjects can reveal interesting patterns in the five modes of data
presented graphically. In FIG. 35, a montage of four mTBI injured
subjects can reveal interesting patterns in the five modes of data
presented graphically. Lastly, a direct comparison of one
non-injured athlete to the injured (mTBI) athlete can provide
observational signatures that can differentiate different groups of
individuals. In FIG. 36, the GSC, SAC, BESS, KD time, and relative
beta power (along the y-axis, respectively from top to bottom) are
each individually stacked on top of each other for each scan visit
(along the x-axis), which is useful in Return-To-Learn,
Return-To-Play, Return-to-Work, Return-to-Duty, and
Return-to-Activity decision making.
Example 3
Rothman Concussion Study
[0147] In collaboration with a clinical practice and a concussion
expert, two groups of subjects were enrolled in an Institutional
Review Board approved clinical protocol, wherein the first group of
subjects (group A) were clinically diagnosed with a concussion
(mTBI) or mild traumatic brain injury and a second control cohort
of subjects (group B) were enrolled who did not have any issue with
concussion and served as Controls (CTL) and were recruited under
the supervision of an Institutional Review Board. Participants from
both groups A and B were scanned identically with an electronic REM
module including a single electrode EEG device as described in PCT
Patent Application No. PCT/US2012/046723, filed Jul. 13, 2012. The
25 minute scan protocol included 1 minute Eyes Closed, 1 minute
Eyes Open, and then approximately 25 minutes of scanning while the
student athlete completed the ImPACT computer test with a head
electronic REM module streaming EEG data to a nearby peripheral MCU
(Dell Vostro 3550 laptop). Key clicks on the peripheral MCU laptop
indicated the temporal beginning and ending of each of the various
tasks within the ImPACT computer assessment. This represents
another multi-modal assessment combining neuropsychological
testing, EEG, and clinical observation in accordance with the
invention.
Example 4
Google Glass implementation of Borealis software
[0148] In collaboration with BrickSimple LLC, we implemented our
Android application software Borealis to run as glassware in the
Google Glass, this enables access to various bio-sensors such as
the built in 3-axis Invensense accelerometer with 3-axis gyrometry,
and 3 axis electronic-compass. This combination of biosensors
enables software running on the Glass to make medical and wellness
measurements and report them in a responsible fashion. We
successfully deployed our app from an Android tablet to the Glass
based "Glassware" and incorporated the accelerometer and eye blink
detection. Furthermore, Glassware based software has been
successfully deployed to the android device and automated pairing
and initiation of the software in a Glass consistent user
interface.
Example 5
Tobii X2-30 Compact Eye Tracker Implementation
[0149] We successfully incorporated a Tobii X2-30 Compact eye
tracker into our data acquisition paradigm. FIG. 37 shows a
schematic of a laptop PC 500 screen but it could equally work for a
tablet or smartphone form factor. The eye tracker 510 is plugged
into a USB port 520 in the present wired mode, but Wi-Fi or other
wireless connectivity is contemplated as well. First, stimuli were
created to check the analytical performance of the eye tracker to
extreme conditions. Numbers were placed on slides in the corners of
the screen and shown for 2 second intervals before moving onto the
next corner in a clockwise rotation. Eye position was plotted for
both eyes averaged as shown in FIG. 38. The output of the eye
tracker very nicely produced the expected trace with the 16:9
aspect ratio apparent in the asymmetric x position and y
position.
[0150] In a follow-up experiment, neuro ophthalmologic saccade
cards (King Devick test) were presented while recording EEG
brainwaves, the microphone and the forward facing webcam on a
laptop. FIG. 39 shows a heat map representation of where the eye
gaze was concentrated in time relative to the stimulation numbers
on the various cards. Thus it is quite clear that while the brain
reads off a number from the car, a fixation in time occurs while
the eye stares at one point in space rather than moving from
fixation to fixation as a saccade. FIG. 40 shows the use of various
predefined Areas of Interest (represented as circles centered on
the numbers on the card) to enable extractable biomarker
measurements of eye gaze that intersect with the AOIs to define
time durations, fixations, and saccade accuracy as the subject
attempts to track the targets of interest. One can see in FIG. 40
the appearance of significant eye gaze taking place "off target" at
the beginning of a given row relative to the end of the same row.
Thus, one can clearly see that the percent accuracy for the first
number on the left of a row is an excellent biomarker as is the
percent of time outside the first number. Less useful would be
those extracted features from the right hand most numbers at the
end of a given row on the cards.
[0151] Those skilled in the art will also appreciate that the
invention may be applied to other applications and may be modified
without departing from the scope of the invention. For example, the
signal processing described herein may be performed on a server, in
the cloud, in the electronics module, or on a local PC, tablet PC,
smartphone, or custom hand held device. Accordingly, the scope of
the invention is not intended to be limited to the exemplary
embodiments described above, but only by the appended claims.
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